{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Solves sparse linear systems in JAX using the KLU algorithm from SuiteSparse, optimized for CPU arrays with float64 or complex128 precision.","skillfed_tags":["sparse-linear-algebra","numerical-computing","jax-integration"],"use_cases":["Solving sparse Jacobian systems in iterative nonlinear solvers (Newton-Raphson, continuation methods)","Transient circuit or PDE simulations where the sparsity pattern is fixed but matrix values and RHS change each timestep","Batched sparse linear solves via jax.vmap over multiple systems with the same structure","Extracting solutions from sparse finite-element or finite-difference discretizations","Embedded in JAX-based optimization or inverse problems requiring repeated sparse system solves"],"what_it_does":"klujax wraps the KLU sparse linear solver from SuiteSparse, enabling efficient solution of sparse linear systems Ax=b within JAX. It accepts sparse matrices in COO format (row indices, column indices, values) and right-hand-side vectors, returning solutions optimized for CPU computation with double precision. The package is designed for scientific computing workflows where sparsity structure is exploited to reduce computation and memory.\n\nThe library supports both simple one-shot solving via `solve()` and advanced patterns for high-performance applications. For transient simulations or iterative methods, you can separate the expensive symbolic analysis (sparsity inspection) and numeric factorization (LU decomposition) steps from the solve step, reusing them across multiple systems. This is critical for performance when the matrix structure or values change frequently but the sparsity pattern remains constant.","worth_installing":"Yes, if you need to solve sparse linear systems in JAX and can work within the constraints. The package is actively maintained, has no known vulnerabilities, and offers significant performance advantages over dense solvers for sparse problems. Install friction is moderate due to compiled dependencies, but pre-built wheels cover common platforms. The LGPL-2.0-only license requires attention if you plan proprietary distribution. Best suited for research, scientific computing, and open-source projects."},"id":"klujax","links":{"html":"https://skillfed.io/packages/klujax","md":"https://skillfed.io/packages/klujax.md","pypi":"https://pypi.org/project/klujax/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-15","license_spdx":"LGPL-2.0-only","license_treatment":"copyleft","name":"klujax","python_support":"supports_current","summary":"a KLU solver for JAX"},"popularity":{"monthly_downloads":166195,"position":10505,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.0"}